Enhancing Autonomous System Security: A Formal Framework for Assessing and Strengthening Autonomous Vehicle Defenses

In recent years, there has been growing concern among experts regarding the risks of hacking autonomous vehicles. As these vehicles become increasingly complex, the number of potential vulnerabilities and challenges associated with securing them also rises. This paper presents a model checking-based framework that utilizes a predefined set of attacks and countermeasures, which are then used to assess the security robustness of the model. First, we formalize a cyber-physical system using Unified Modeling Language (UML) class and activity diagrams. Subsequently, we employ UML to develop a meta-language for autonomous vehicle systems, cyberattacks, and cybersecurity countermeasures. The framework instantiates domain-specific application diagrams for autonomous vehicles, identifies existing attack surfaces, and generates potential attacks that could exploit detected vulnerabilities or weaknesses. Furthermore, the proposed framework generates appropriate Java code for integrating countermeasures, attacks, and smart vehicle models. To demonstrate the effectiveness of the proposed solution, we model, analyze, harden, and evaluate our framework using a real-world use case. This research aims to contribute to the ongoing efforts to improve the security of autonomous vehicles and mitigate the risks associated with hacking and other cyber threats. By applying the framework presented in this paper, the goal is to promote a more secure development and implementation of autonomous vehicle systems.